CAJ | 학술논문

在故障诊断中，将高维特征空间压缩到低维特征空间可以简化故障分类器设计，提高运算效率。研究了自适应遗传算法（AGA）和粗糙集（RS）理论在特征选择和特征约简中的应用，并针对柴油机燃油喷射系统故障提取了简化特征，建立了神经网络模型。试验结果表明，基于AGA-RS的故障特征参数提取方法可使故障分类器输入参数同时具有有效性和简约性，提高了神经网络的运算效率。
재고장진단중，장고유특정공간압축도저유특정공간가이간화고장분류기설계，제고운산효솔。연구료자괄응유전산법（AGA）화조조집（RS）이론재특정선택화특정약간중적응용，병침대시유궤연유분사계통고장제취료간화특정，건립료신경망락모형。시험결과표명，기우AGA-RS적고장특정삼수제취방법가사고장분류기수입삼수동시구유유효성화간약성，제고료신경망락적운산효솔。
In fault diagnosis, compressing high dimension feature space to low dimension feature space can simplifiy the design of fault classifier and improve computational efficiency. Adaptive genetic algorithm and rough sets theory are researched for feature selection and reduction, the brief features are abstracted according to the faults of diesel fuel injection system, and the neural network model is built. Experimental results indicate the method can extract effective and brief features, but also improve the capacity of neural network.